// Ceres Solver - A fast non-linear least squares minimizer

// Copyright 2015 Google Inc. All rights reserved.

// http://ceres-solver.org/

//

// Redistribution and use in source and binary forms, with or without

// modification, are permitted provided that the following conditions are met:

//

// * Redistributions of source code must retain the above copyright notice,

//   this list of conditions and the following disclaimer.

// * Redistributions in binary form must reproduce the above copyright notice,

//   this list of conditions and the following disclaimer in the documentation

//   and/or other materials provided with the distribution.

// * Neither the name of Google Inc. nor the names of its contributors may be

//   used to endorse or promote products derived from this software without

//   specific prior written permission.

//

// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"

// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE

// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE

// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE

// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR

// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF

// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS

// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN

// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)

// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE

// POSSIBILITY OF SUCH DAMAGE.

//

// Author: sameeragarwal@google.com (Sameer Agarwal)

#include "ceres/partitioned_matrix_view.h"

#include <vector>

#include "ceres/block_structure.h"

#include "ceres/casts.h"

#include "ceres/internal/eigen.h"

#include "ceres/internal/scoped_ptr.h"

#include "ceres/linear_least_squares_problems.h"

#include "ceres/random.h"

#include "ceres/sparse_matrix.h"

#include "glog/logging.h"

#include "gtest/gtest.h"

namespace ceres {

namespace internal {

const double kEpsilon = 1e-14;

class PartitionedMatrixViewTest : public ::testing::Test {

 protected :

  virtual void SetUp() {

    srand(5);

    scoped_ptr<LinearLeastSquaresProblem> problem(

        CreateLinearLeastSquaresProblemFromId(2));

    CHECK_NOTNULL(problem.get());

    A_.reset(problem->A.release());

    num_cols_ = A_->num_cols();

    num_rows_ = A_->num_rows();

    num_eliminate_blocks_ = problem->num_eliminate_blocks;

    LinearSolver::Options options;

    options.elimination_groups.push_back(num_eliminate_blocks_);

    pmv_.reset(PartitionedMatrixViewBase::Create(

                   options,

                   *down_cast<BlockSparseMatrix*>(A_.get())));

  }

  int num_rows_;

  int num_cols_;

  int num_eliminate_blocks_;

  scoped_ptr<SparseMatrix> A_;

  scoped_ptr<PartitionedMatrixViewBase> pmv_;

};

TEST_F(PartitionedMatrixViewTest, DimensionsTest) {

  EXPECT_EQ(pmv_->num_col_blocks_e(), num_eliminate_blocks_);

  EXPECT_EQ(pmv_->num_col_blocks_f(), num_cols_ - num_eliminate_blocks_);

  EXPECT_EQ(pmv_->num_cols_e(), num_eliminate_blocks_);

  EXPECT_EQ(pmv_->num_cols_f(), num_cols_ - num_eliminate_blocks_);

  EXPECT_EQ(pmv_->num_cols(), A_->num_cols());

  EXPECT_EQ(pmv_->num_rows(), A_->num_rows());

}

TEST_F(PartitionedMatrixViewTest, RightMultiplyE) {

  Vector x1(pmv_->num_cols_e());

  Vector x2(pmv_->num_cols());

  x2.setZero();

  for (int i = 0; i < pmv_->num_cols_e(); ++i) {

    x1(i) = x2(i) = RandDouble();

  }

  Vector y1 = Vector::Zero(pmv_->num_rows());

  pmv_->RightMultiplyE(x1.data(), y1.data());

  Vector y2 = Vector::Zero(pmv_->num_rows());

  A_->RightMultiply(x2.data(), y2.data());

  for (int i = 0; i < pmv_->num_rows(); ++i) {

    EXPECT_NEAR(y1(i), y2(i), kEpsilon);

  }

}

TEST_F(PartitionedMatrixViewTest, RightMultiplyF) {

  Vector x1(pmv_->num_cols_f());

  Vector x2 = Vector::Zero(pmv_->num_cols());

  for (int i = 0; i < pmv_->num_cols_f(); ++i) {

    x1(i) = RandDouble();

    x2(i + pmv_->num_cols_e()) = x1(i);

  }

  Vector y1 = Vector::Zero(pmv_->num_rows());

  pmv_->RightMultiplyF(x1.data(), y1.data());

  Vector y2 = Vector::Zero(pmv_->num_rows());

  A_->RightMultiply(x2.data(), y2.data());

  for (int i = 0; i < pmv_->num_rows(); ++i) {

    EXPECT_NEAR(y1(i), y2(i), kEpsilon);

  }

}

TEST_F(PartitionedMatrixViewTest, LeftMultiply) {

  Vector x = Vector::Zero(pmv_->num_rows());

  for (int i = 0; i < pmv_->num_rows(); ++i) {

    x(i) = RandDouble();

  }

  Vector y = Vector::Zero(pmv_->num_cols());

  Vector y1 = Vector::Zero(pmv_->num_cols_e());

  Vector y2 = Vector::Zero(pmv_->num_cols_f());

  A_->LeftMultiply(x.data(), y.data());

  pmv_->LeftMultiplyE(x.data(), y1.data());

  pmv_->LeftMultiplyF(x.data(), y2.data());

  for (int i = 0; i < pmv_->num_cols(); ++i) {

    EXPECT_NEAR(y(i),

                (i < pmv_->num_cols_e()) ? y1(i) : y2(i - pmv_->num_cols_e()),

                kEpsilon);

  }

}

TEST_F(PartitionedMatrixViewTest, BlockDiagonalEtE) {

  scoped_ptr<BlockSparseMatrix>

      block_diagonal_ee(pmv_->CreateBlockDiagonalEtE());

  const CompressedRowBlockStructure* bs  = block_diagonal_ee->block_structure();

  EXPECT_EQ(block_diagonal_ee->num_rows(), 2);

  EXPECT_EQ(block_diagonal_ee->num_cols(), 2);

  EXPECT_EQ(bs->cols.size(), 2);

  EXPECT_EQ(bs->rows.size(), 2);

  EXPECT_NEAR(block_diagonal_ee->values()[0], 10.0, kEpsilon);

  EXPECT_NEAR(block_diagonal_ee->values()[1], 155.0, kEpsilon);

}

TEST_F(PartitionedMatrixViewTest, BlockDiagonalFtF) {

  scoped_ptr<BlockSparseMatrix>

      block_diagonal_ff(pmv_->CreateBlockDiagonalFtF());

  const CompressedRowBlockStructure* bs  = block_diagonal_ff->block_structure();

  EXPECT_EQ(block_diagonal_ff->num_rows(), 3);

  EXPECT_EQ(block_diagonal_ff->num_cols(), 3);

  EXPECT_EQ(bs->cols.size(), 3);

  EXPECT_EQ(bs->rows.size(), 3);

  EXPECT_NEAR(block_diagonal_ff->values()[0], 70.0, kEpsilon);

  EXPECT_NEAR(block_diagonal_ff->values()[1], 17.0, kEpsilon);

  EXPECT_NEAR(block_diagonal_ff->values()[2], 37.0, kEpsilon);

}

}  // namespace internal

}  // namespace ceres